面向城市道路的智能网联汽车多车道轨迹优化方法

Multi-lane Trajectory Optimization for Intelligent Connected Vehicles in Urban Road Network

为提高城市路网下智能网联汽车的通行效率以及燃油效率,提出面向城市道路的多车道时空轨迹优化方法。首先,结合多车道时空位置关系定义智能网联汽车状态与约束,综合考虑通行效率与燃油经济性构建时空轨迹复合优化模型,并采用庞特里亚金极大值算法进行求解。然后,本文设定协同换道的规则,并通过Q-learning算法获取最优的换道策略。最后,通过SUMO/Python联合仿真验证了该方法可以在不同车辆饱和程度、绿信比状态及最低通行速度条件下有效提高通行效率,且燃油效率得到明显改善。

In order to improve the traffic efficiency and fuel utilization efficiency of intelligent connected vehicles(ICVs)under urban traffic networks,a multilane spatiotemporal trajectory optimization method is proposed in this paper.Firstly,the state and constraints of the ICVs are defined based on the multi-lane spatiotemporal posi⁃tion relationship and the compound optimization model of spatiotemporal trajectory is constructed by considering the traffic efficiency and fuel economy,which is solved by the Pontryagin Maximum algorithm.Furthermore,the rules of cooperative lane change are designed to obtain the optimal lane change strategy by Q-learning algorithm.Finally,the SUMO/Python co-simulation tests show that the method can effectively improve the traffic efficiency under differ⁃ent vehicle saturation levels,split allocation,and minimum traffic speed conditions,with great improvement of fuel efficiency.